The use of geospatial or overhead imagery has increased in recent years and represents a significant tool that may be utilized in a number of contexts. As such, high quality geospatial imagery has become increasingly valuable. For example, a variety of different entities (e.g., individuals, governments, corporations, or others) utilize geospatial imagery (e.g., satellite imagery) for a multitude of applications in a variety of contexts.
With increasingly capable satellites being commissioned and launched, very high resolution (VHR) remotely-sensed multispectral Earth imagery has become increasingly available and useable. For example, as the number of satellite image acquisition systems in operation grows, acquisition ability and flexibility improves. In an example, DigitalGlobe, Inc. of Longmont, Colo. currently operates a number of satellites including, IKONOS, GeoEye-1, QuickBird, WorldView 1, WorldView 2, and WorldView 3. Accordingly, around the clock global coverage may be achieved through the satellite constellation currently in operation. For instance, the DigitalGlobe constellation of satellites can image the entire Earth's landmass every 75 days and may capture over six times the Earth's landmass every year with a capacity to collect at least 2.6 million square kilometers of imagery a day. With selective tasking, DigitalGlobe's satellite constellation may quickly and continually collect imagery from targeted locations to provide near real time feedback on global events or the like.
Furthermore, the resolution of image acquisition satellites also continues to increase. For instance, currently operated satellites may acquire images with a maximum spatial resolution of 50 cm (wherein each pixel in the images corresponds with the distance measure of the spatial resolution). Additionally, planned satellite launches may provide even greater resolution capabilities with spatial resolutions as high as about 30 cm or greater (i.e., less than 30 cm, such as 25 cm, 15 cm, or lower).
In this light, the amount and quality of VHR remotely-sensed multispectral Earth imagery continues to increase as does the amount and types of image data collected. Accordingly, the nature of the VHR remotely-sensed multispectral Earth imagery may facilitate uses beyond simply providing pixels as image data. For instance, higher level data processing may be applied to the images to, for example, identify objects, identify textures, or otherwise extract useful data from the raw image data. One such application has been in land use identification and land classification, where remotely-sensed images are analyzed to categorize pixels of an image into a category of land use or land class. As just one example, image pixels can be categorized or analyzed to identify and/or characterize areas of the urban settlement (e.g., the urban “build-up” or “built-up,” such as three-dimensional man-made structures or the like).
As the amount of image data that is available grows and the nature of the image data acquired changes and is improved, advanced image data processing and image analytics are needed to keep pace with the advances in image acquisition technology to facilitate new techniques applied to acquired images to expand the number of applications for which such technology may be used.